Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
FaceController: Controllable Attribute Editing for Face in the Wild
Authors: Zhiliang Xu, Xiyu Yu, Zhibin Hong, Zhen Zhu, Junyu Han, Jingtuo Liu, Errui Ding, Xiang Bai3083-3091
AAAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 16,833 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,968 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive quantitative and qualitative evaluations have been conducted. In a single framework, our method achieves the best or competitive scores on a variety of face applications. Experiments Implementation details. The training images for Face Controller are collected from Celeb A-HQ (Karras et al. 2017), FFHQ (Karras, Laine, and Aila 2019), and VGGFace (Parkhi, Vedaldi, and Zisserman 2015) datasets. |
| Researcher Affiliation | Collaboration | 1 Huazhong University of Science and Technology 2 Baidu Inc. |
| Pseudocode | No | Not found. |
| Open Source Code | No | Not found. The paper only links to code for a comparison method (Face Swap) not their own. |
| Open Datasets | Yes | The training images for Face Controller are collected from Celeb A-HQ (Karras et al. 2017), FFHQ (Karras, Laine, and Aila 2019), and VGGFace (Parkhi, Vedaldi, and Zisserman 2015) datasets. |
| Dataset Splits | No | The paper uses some training data for face reconstruction (20%) but does not specify overall train/validation/test splits for model training, nor does it specify how the datasets mentioned (Celeb A-HQ, FFHQ, VGGFace) are split for training and validation. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models) are mentioned. |
| Software Dependencies | No | The paper mentions software like 'PyTorch' and 'Bi Se Net model' and 'VGG network' but does not specify their version numbers. |
| Experiment Setup | Yes | The generator and discriminator are trained around 500K steps, respectively. More details can be found in the Supplementary Materials. ... L = Ladv + λid Lid + λlm Llm + λhm Lhm + λper Lper, (9) where Ladv denotes GAN loss. We set the λid = 10, λlm = 10000, λhm = 100, and λper = 100, respectively. |